Associations of small vessel disease and acute symptomatic seizures in ischemic stroke patients.

BACKGROUND AND PURPOSE: Cerebral microbleeds (CMBs), markers of small vessel disease are frequent in ischemic stroke, yet the association with acute symptomatic seizures (ASS) has not been well characterized. METHODS: A retrospective cohort of hospitalized patients with anterior circulation ischemic stroke. The association of CMBs with acute symptomatic seizures was assessed using a logistic regression model and causal mediation analysis. RESULTS: Of 381 patients, 17 developed seizures. Compared with patients without CMBs, those with CMBs had a three-fold higher unadjusted odds of seizures (unadjusted OR: 3.84, 95% 1.16-12.71, p = 0.027). After adjusting for confounders such as stroke severity, cortical infarct location, and hemorrhagic transformation, the association between CMBs and ASS was attenuated (adjusted OR: 3.11, 95%CI: 0.74-11.03, p = 0.09). The association was not mediated by stroke severity. CONCLUSION: In this cohort of hospitalized patients with anterior circulation ischemic stroke, CMBs were more likely to be found in patients with ASS than those without ASS, an association that was attenuated when accounting for stroke severity, cortical infarct location, and hemorrhagic transformation. Evaluation of the long-term risk of seizures associated with CMBs and other markers of small vessel disease is warranted.

Association of the Gut Microbiota With Cognitive Function in Midlife.

Importance: Animal experiments and small clinical studies support a role for the gut microbiota in cognitive functioning. Few studies have investigated gut microbiota and cognition in large community samples. Objective: To examine associations of gut microbial composition with measures of cognition in an established population-based study of middle-aged adults. Design, Setting, and Participants: This cross-sectional study analyzed data from the prospective Coronary Artery Risk Development in Young Adults (CARDIA) cohort in 4 US metropolitan centers between 2015 and 2016. Data were analyzed in 2019 and 2020. Exposures: Stool DNA were sequenced, and the following gut microbial measures were gathered: (1) ß-diversity (between-person) derived with multivariate principal coordinates analysis; (2) α-diversity (within-person), defined as richness (genera count) and the Shannon index (integrative measure of genera richness and evenness); and (3) taxonomy (107 genera, after filtering). Main Outcomes and Measures: Cognitive status was assessed using 6 clinic-administered cognitive tests: Montreal Cognitive Assessment (MoCA), Digit Symbol Substitution Test (DSST), Rey-Auditory Verbal Learning Test (RAVLT), Stroop, category fluency, and letter fluency. A global score measure derived using principal components analysis was also assessed; the first principal component explained 56% of variability. Results: Microbiome data were available on 597 CARDIA participants; mean (SD) age was 55.2 (3.5) years, 268 participants (44.7%) were men, and 270 (45.2%) were Black. In multivariable-adjusted principal coordinates analysis, permutational multivariate analysis of variance tests for ß-diversity were statistically significant for all cognition measures (principal component analysis, P = .001; MoCA, P = .001; DSST, P = .001; RAVLT, P = .001; Stroop, P = .007; category fluency, P = .001) with the exception of letter fluency (P = .07). After adjusting for sociodemographic variables (age, race, sex, education), health behaviors (physical activity, diet, smoking, medication use), and clinical covariates (body mass index, diabetes, hypertension), Barnesiella was positively associated with the first principal component (ß, 0.16; 95% CI, 0.08-0.24), DSST (ß, 1.18; 95% CI, 0.35-2.00), and category fluency (ß, 0.59; 95% CI, 0.31-0.87); Lachnospiraceae FCS020 group was positively associated with DSST (ß, 2.67; 95% CI, 1.10-4.23), and Sutterella was negatively associated with MoCA (ß, -0.27; 95% CI, -0.44 to -0.11). Conclusions and Relevance: In this cross-sectional study, microbial community composition, based on ß-diversity, was associated with all cognitive measures in multivariable-adjusted analysis. These data contribute to a growing body of literature suggesting that the gut microbiota may be associated with cognitive aging, but must be replicated in larger samples and further researched to identify relevant pathways.

Factors Associated With Prolonged Length of Stay in Patients Hospitalized With Generalized Convulsive Status Epilepticus in the United States.

OBJECTIVE: Generalized convulsive status epilepticus (GCSE) is a severe complication of epilepsy, which typically requires extended hospitalization, resulting in substantial resource utilization, hospital expenditures, and patient costs. In this nationwide analysis, we examined hospital length of stay (LOS) patterns for GCSE, and the factors that influence prolonged LOS. METHODS: We extracted data for adult patients (age 18 years and above) with a primary discharge diagnosis of GCSE from the National Inpatient Sample (NIS) from 2006-2014, the largest all-payer inpatient care database in the United States. We computed LOS (≤1, 2-6, and ≥7 days), overall, and across pre-specified patient-related, hospital-related, and healthcare system-related variables available in the NIS. We identified factors independently associated with prolonged hospitalization (2 or more days), using a multivariable logistic regression model. RESULTS: Of 57,832 discharged with a primary diagnosis of GCSE, 6,133 (10.7%) had a LOS ≤1 day, 27,327 (7.3%) stayed for 2-6 days, and 24,372 (42.1%) stayed for ≥7 days. After adjusting for confounders, patients who were older, female, Black, and Hispanic, who underwent continuous EEG video monitoring, were Medicare beneficiaries, had medical comorbidities, or were admitted to large/urban hospitals, were all significantly more likely to have prolonged LOS. CONCLUSION: Over 40% of patients hospitalized for GCSE in the United States spend at least a week in the hospital. Efforts to shorten hospitalization for GCSE may need to primarily focus on patient groups with select sociodemographic and clinical characteristics.

Virtual Shadowing Program for Preclinical Medical Students.

Our annual summer shadowing program for preclinical medical students faced significant challenges due to COVID-19-related safety and resource concerns during Summer 2020. We created a pilot 7-week virtual shadowing program with the goal of providing virtual observational clinical experiences to increase students' clinical exposure and understanding of medical specialties. Faculty and preclinical medical students were matched via student preference selection and mentor availability. A practice guide was developed that outlined suggested virtual shadowing procedures. Afterward, participating faculty and students were surveyed on their experience. Overall, both faculty and students found the program effective and experienced limited technological difficulty.

Risk models to predict late-onset seizures after stroke: A systematic review.

BACKGROUND AND PURPOSE: We performed a systematic review to evaluate available risk models to predict late seizure onset among stroke survivors. METHODS: We searched major databases (PubMed, SCOPUS, and Cochrane Library) from inception to October 2020 for articles on the development and/or validation of risk models to predict late seizures after a stroke. The impact of models to predict late-onset seizures was also assessed. We included seven articles in the final analysis. For each of these studies, we evaluated the study design and scope of predictors analyzed to derive each model. We assessed the performance of the models during internal and external validation in terms of discrimination and calibration. RESULTS: Three studies focused on ischemic stroke alone, with c-statistic values ranging from 0.73 to 0.77. The SeLECT model from Switzerland was externally validated in Italian, German, and Austrian cohorts where c-statistics ranged from 0.69 to 0.81. This model along with the PSEiCARe model, were internally validated and calibration performance was provided for both models. The CAVS and CAVE models reported on the risk of late-onset seizures in patients with hemorrhagic stroke. The CAVS model derivation cohort was racially diverse. The CAVS model's c-statistic was 0.76, while the CAVE model had a c-statistic of 0.81. Calibration and internal validation were not performed for either study. The CAVS model, created from a Finnish population, was externally validated in American and French cohorts, with c-statistics of 0.73 and 0.69, respectively. Finally, the two studies focusing on both types of stroke came from the PoSERS and INPOSE models. Neither model provided c-statistics, calibration metrics, internal or external validation information. We found no evidence of the presence of impact studies to assess the effect of adopting late-onset seizure risk models after stroke on clinical outcomes. CONCLUSION: The SeLECT model was the only model developed in line with proposed guidelines for appropriate model development. The model, which was externally validated in a very similar and homogeneous population, may need to be tested in a more racially/ethnic diverse and younger population; testing the SeLECT model, accounting for overall brain health is likely to improve the identification of high-risk patients for late post stroke seizures.